We present a new framework for task-incremental learning, which learns to expand a joint network for multiple tasks dynamically, based on two new ideas: (a) exploit task similarity by binding an incoming task with the network of an existing, similar task, and (b) grow the layers relative to the similar task, by estimating a hierarchical conflict distribution between the layers of the similar and new tasks. The method enables multi-objective (performance, size) optimization across the space of shared, multi-task architectures.
There are several directions for future work: exploring techniques to scale BO to large multi-task benchmarks [1]}, [2]}, investigating other representation similarity techniques [3]}, enabling expansion for non-symmetrical task networks, and adding layer adaptation to our algorithm.
